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by sky2224
739 days ago
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Would you happen to know of a good resource that shows an implementation of an LLM so I can see this more concretely in practice? The idea of an LLM makes sense: it's ultimately a graph with statistical weights for which node we use to generate text next (my understanding here is still correct, yes?). My issue is, where does the "learning" part come in? It feels like it's all hard-coded but I know it's not. What allows for flexibility in token generation besides that randomization from temperature that you mentioned? |
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During training, you update the weights. As a very, very simple example in relation to my original response above... Suppose you are training a neural network. You know that the probability of "weary" coming after "Once upon a midnight dreary, while I pondered, " is 1.00, right? So you set up your LLM with random weights, and you see that the probability estimate for "weary" is 0.77 (I'm making this # up)... Well you can now use an algorithm to nudge the neural network's weights in a way that makes the 0.77 closer to 1.00!
In other words -- the learning happens during training.
A good place to start might be this course: https://karpathy.ai/zero-to-hero.html
Depending on your sophistication and amount of free time you have, here are more resources: https://phaseai.com/resources/free-resources-ai-ml-2024
Good luck!